标题： PSO-Net：基于注意力机制的可解释深度神经网络的银屑病自动评估系统开发 显示英文标题

标题： PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks

摘要： 银屑病是一种需要长期治疗和监测的慢性皮肤病。尽管银屑病面积和严重程度指数（PASI）被用作临床试验中评估银屑病严重程度的标准测量方法，但它存在许多缺点，例如（1）患者因亲自到诊所接受评估而承受负担，（2）调查者评分所需的时间以及（3）评分者之间和评分者内部评分的变异性。 为了解决这些缺点，我们提出了一种新的可解释的深度学习架构，称为PSO-Net，它将来自不同解剖区域的数字图像映射以得出基于注意力的评分。区域分数进一步结合以估计绝对PASI分数。此外，我们设计了一种新的回归激活图，通过排名注意力分数来提高可解释性。使用这种方法，我们分别获得了与两位不同临床医生评分者的类间相关性评分为82.2% [95% 置信区间：77%-87%] 和87.8% [95% 置信区间：84%-91%]。 显示英文摘要

摘要： Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is utilized as a standard measurement to assess psoriasis severity in clinical trials, it has many drawbacks such as (1) patient burden for in-person clinic visits for assessment of psoriasis, (2) time required for investigator scoring and (3) variability of inter- and intra-rater scoring. To address these drawbacks, we propose a novel and interpretable deep learning architecture called PSO-Net, which maps digital images from different anatomical regions to derive attention-based scores. Regional scores are further combined to estimate an absolute PASI score. Moreover, we devise a novel regression activation map for interpretability through ranking attention scores. Using this approach, we achieved inter-class correlation scores of 82.2% [95% CI: 77- 87%] and 87.8% [95% CI: 84-91%] with two different clinician raters, respectively.